Customer Acquisition via Display Advertising Using Multi-Armed Bandit Experiments

By Eric M. Schwartz, Eric T. Bradlow, Peter S. Fader. Marketing science (Providence, R.I.), 2017

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A Tutorial on Thompson Sampling (2017, background): “Adaptations of Thompson sampling have now been successfully applied in a wide variety of domains, including revenue management [7], marketing [8], Monte Carlo tree search [9], A/B testing [10], Internet advertising [10, 11, 12], recommendation systems [13], hyperparameter tuning [14], and arcade gam”
Machine learning and AI in marketing – Connecting computing power to human insights (2020, unknown): “Along a similar line, Schwartz, Bradlow, and Fader (2017) conducted ﬁ eld experiments for online display advertising by formulating the advertiser's problem as a MAB problem, and showed that the reinforcement learning policy achieved an 8% improvement in customer acquisition.”
Artificial Intelligence and Marketing: Pitfalls and Opportunities (2020, unknown): “…(Hauser, Liberali, & Urban, 2014; Hauser, Urban, Guy, & Braun, 2009; Urban, Liberali, MacDonald, Bordley, & Hauser, 2014), online advertising (Baardman, Fata, Pani, & Perakis, 2019; Schwartz, Bradlow, & Fader, 2017), and pricing problems (Misra, Schwartz, & Abernethy, 2018) using this approach.”